This script merges CalMAPPER activity data with treatment polygons. This is a necessary step before analyzing prescribed fire data in CalMAPPER.
calmapper_dir <- fs::dir_ls(ref_path, recurse = T, glob = '*CalMAPPER', type = 'directory')
calmapper_gdb <- fs::dir_ls(calmapper_dir, recurse = T, glob = '*.gdb')
if(length(calmapper_gdb) > 1){
stop( "There's more than one CalMAPPER .gdb file. script assumes only 1.")
}
st_layers(calmapper_gdb)
## Driver: OpenFileGDB
## Available layers:
## layer_name geometry_type features fields
## 1 CMDash_ProjectTreatments Multi Polygon 1560 21
## 2 CMDash_TreatmentPols Multi Polygon 2784 23
## 3 CMDash_TreatmentLines Multi Line String 23 22
## 4 CMDash_TreatmentPnts Multi Point 136 20
## 5 CMDash_Activities NA 20439 38
## 6 CMDash_Metadata NA 1 7
## 7 CalMapper_fire_act Multi Polygon 1861 28
## crs_name
## 1 WGS 84 / Pseudo-Mercator
## 2 WGS 84 / Pseudo-Mercator
## 3 WGS 84 / Pseudo-Mercator
## 4 WGS 84 / Pseudo-Mercator
## 5 <NA>
## 6 <NA>
## 7 WGS 84 / Pseudo-Mercator
act <- st_read(calmapper_gdb, layer = 'CMDash_Activities')
## Reading layer `CMDash_Activities' from data source
## `C:\Users\ctubbesi\OneDrive - California Air Resources Board\Documents\Reference data\CalMAPPER\CALFIRE_FuelReductionProjects_2023\CALFIRE_FuelReductionProjects.gdb'
## using driver `OpenFileGDB'
## Warning: no simple feature geometries present: returning a data.frame or tbl_df
trt <- st_read(calmapper_gdb, layer = 'CMDash_TreatmentPols')
## Reading layer `CMDash_TreatmentPols' from data source
## `C:\Users\ctubbesi\OneDrive - California Air Resources Board\Documents\Reference data\CalMAPPER\CALFIRE_FuelReductionProjects_2023\CALFIRE_FuelReductionProjects.gdb'
## using driver `OpenFileGDB'
## Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet, : GDAL
## Message 1: organizePolygons() received a polygon with more than 100 parts. The
## processing may be really slow. You can skip the processing by setting
## METHOD=SKIP, or only make it analyze counter-clock wise parts by setting
## METHOD=ONLY_CCW if you can assume that the outline of holes is counter-clock
## wise defined
## Simple feature collection with 2784 features and 23 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -13842530 ymin: 3842624 xmax: -12941530 ymax: 5161286
## Projected CRS: WGS 84 / Pseudo-Mercator
act <- act %>%
filter(!ACTIVITY_DESCRIPTION %in% c("GIS Validation", "Education Outreach", "Project Administration", "Planning Meeting", "Public Contacts", "Water Site Development"))
act <- act %>%
filter(ACTIVITY_STATUS == "Complete", UNIT_OF_MEASURE == "Acres")
act <- act %>%
select(-UNIT_OF_MEASURE, -PROJECT_STATUS, -QUANTITY, -ACTIVITY_STATUS, -GROUND_DISTURBING, -ThisFiscal, -LastFiscal, -PrevFiscals, -OBJECTID_1, -CFIP_FR, -CONTRACT)
act_sf <- right_join(trt %>% select(PROJECT_ID, TREATMENT_ID),
act)
## Joining with `by = join_by(PROJECT_ID, TREATMENT_ID)`
act_sf %>%
st_drop_geometry() %>%
group_by(ACTIVITY_DESCRIPTION) %>%
count() %>%
print(n=50)
## # A tibble: 29 × 2
## # Groups: ACTIVITY_DESCRIPTION [29]
## ACTIVITY_DESCRIPTION n
## <chr> <int>
## 1 Broadcast Burn 545
## 2 Chaining 57
## 3 Chipping 1701
## 4 Commercial Thinning (Cable Yarding) 18
## 5 Commercial Thinning (Tractor Yarding) 24
## 6 Crushing 35
## 7 Erosion Control 5
## 8 Follow up - Herbicide 56
## 9 Follow up - Other 6
## 10 Follow up - Slash disposal 170
## 11 Fuel Break (Shaded) 25
## 12 Grazing 23
## 13 Herbicide (Post-Treatment) 26
## 14 Herbicide (Pre-Treatment) 8
## 15 Land Conservation 4
## 16 Lop and Scatter 521
## 17 Mastication 766
## 18 Pile Burning 1316
## 19 Piling (Manual) 1919
## 20 Piling (Mechanical) 251
## 21 Pruning 305
## 22 Rangeland Mowing 55
## 23 Release - Herbicide 15
## 24 Release - Mechanical 93
## 25 Release - Other 10
## 26 Site Preparation (CFIP) 68
## 27 Thinning 285
## 28 Thinning (Manual) 2903
## 29 Thinning (Mechanical) 200
act_sf_fire <-
act_sf %>%
filter(ACTIVITY_DESCRIPTION %in% c("Broadcast Burn", "Cultural Burning", "Pile Burning"))
test <- act_sf_fire %>%
mutate(ACTIVITY_START_test = as.Date(ACTIVITY_START)) %>%
mutate(ACTIVITY_END_test = as.Date(ACTIVITY_END))
This should be 2/11 - 2/11. The values that R automatically reads in from ArcGIS are one day off.
test %>%
filter(AQ_ID == 69461) %>%
select(ACTIVITY_START, ACTIVITY_START_test, ACTIVITY_END, ACTIVITY_END_test)
## Simple feature collection with 1 feature and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -13709070 ymin: 4748703 xmax: -13705400 ymax: 4751503
## Projected CRS: WGS 84 / Pseudo-Mercator
## ACTIVITY_START ACTIVITY_START_test ACTIVITY_END ACTIVITY_END_test
## 1 2020-02-10 16:00:00 2020-02-11 2020-02-10 16:00:00 2020-02-11
## SHAPE
## 1 MULTIPOLYGON (((-13706330 4...
act_sf_fire <- act_sf_fire %>%
mutate(ACTIVITY_START = as.Date(ACTIVITY_START)) %>%
mutate(ACTIVITY_END = as.Date(ACTIVITY_END))
act_sf_fire <- act_sf_fire %>%
mutate(DURATION = ACTIVITY_END - ACTIVITY_START+1)
act_sf_fire <- act_sf_fire %>%
mutate(YEAR = year(ACTIVITY_END))
act_sf_fire %>%
filter(AQ_ID == 69461) %>%
select(ACTIVITY_START, ACTIVITY_END, DURATION, YEAR)
## Simple feature collection with 1 feature and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -13709070 ymin: 4748703 xmax: -13705400 ymax: 4751503
## Projected CRS: WGS 84 / Pseudo-Mercator
## ACTIVITY_START ACTIVITY_END DURATION YEAR SHAPE
## 1 2020-02-11 2020-02-11 1 days 2020 MULTIPOLYGON (((-13706330 4...
cm <- act_sf_fire
save(cm, file = here("Rdata/CalMapper_activities_fire.Rdata"))
write.csv(cm, file = here("excel/CalMapper_activities_fire.csv"), row.names = F)
write.csv(act_sf_fire %>% st_drop_geometry(), file = "~/Reference data/CalMapper/activities_fire.csv", row.names = F)
write.csv(trt %>% st_drop_geometry(), file = "~/Reference data/CalMapper/treatments_fire.csv", row.names = F)
act_broadcast <- act_sf_fire %>%
filter(ACTIVITY_DESCRIPTION == "Broadcast Burn")
trt %>%
st_drop_geometry() %>%
group_by(TREATMENT_OBJECTIVE) %>%
count()
## # A tibble: 5 × 2
## # Groups: TREATMENT_OBJECTIVE [5]
## TREATMENT_OBJECTIVE n
## <chr> <int>
## 1 Broadcast Burn 559
## 2 Forestland Stewardship 296
## 3 Fuel Break 215
## 4 Fuel Reduction 1542
## 5 Right of Way Clearance 172
trt_broadcast_complete <- trt %>%
filter(TREATMENT_OBJECTIVE == "Broadcast Burn") %>%
filter(ACTIVITY_STATUS == "Complete")
act_broadcast that
aren’t in trtcheck <- act_broadcast %>%
filter(!TREATMENT_ID %in% trt$TREATMENT_ID) %>%
nrow()
check2 <- act_broadcast %>%
filter(!TREATMENT_NAME %in% trt$TREATMENT_NAME) %>%
nrow()
if(check==0 & check2 == 0){
paste("There are no TREATMENT_ID values or TREATMENT_NAME values in activities that aren't in trt, at least for broadcast burns")
} else{
print("Alert! There are TREATMENT_ID or TREATMENT_NAME values in activities data that aren't in trt")
}
## [1] "There are no TREATMENT_ID values or TREATMENT_NAME values in activities that aren't in trt, at least for broadcast burns"
not_in_act <- trt_broadcast_complete %>%
filter(!TREATMENT_NAME %in% act_broadcast$TREATMENT_NAME) %>%
nrow()
print(paste("Alert! There are", not_in_act, "records in trt_broadcast_complete that aren't in activities. These are potential missing data from the final output!"))
## [1] "Alert! There are 7 records in trt_broadcast_complete that aren't in activities. These are potential missing data from the final output!"
missing_from_activities <- trt_broadcast_complete %>%
filter(!TREATMENT_ID %in% act_broadcast$TREATMENT_ID) %>%
select(CALMAPPER_ID, PROJECT_NAME, TREATMENT_NAME, TREATMENT_OBJECTIVE, PROJECT_STATUS, ACTIVITY_STATUS, PROJECT_START_DATE, PROJECT_END_DATE, TREATMENTAREA_ACRES)
missing_from_activities %>%
st_drop_geometry() %>%
summarize(missing_acres = sum(TREATMENTAREA_ACRES))
## missing_acres
## 1 653.99
write_excel_csv(missing_from_activities %>% st_drop_geometry(), "calmapper_trt_not_act.xls")
map <- mapview(list(trt_broadcast_complete, act_broadcast, missing_from_activities), col.regions=list("red","blue", "green"),col=list("red","blue", "green"))
map